Agricultural industry represents one of the most important human activities for tens of thousands of years. The current trend in agriculture is so-called Agriculture 4.0. Precision farming techniques have potential for use in a range of areas that lead to more effective management of strategic decision-making in agricultural supply chains. One of the most important attributes of agricultural products is their price, quantity and quality. These attributes are influenced by a number of factors from hydrometeorological conditions to the methods of pest and disease control. This paper is focused on finding potential role of hydrometeorological data in agricultural supply chain. Our main focus is to create crop yield estimation model of selected cereal crops based on hydrometeorological indices and NDVI using various machine learning techniques.